[R-sig-ME] Best way to handle missing data?

[David Duffy]

On Mon, 2 Mar 2015, Bonnie Dixon wrote:

> I don't think the model I am working on is a good candidate for structural
> equation modeling because the data set is very unbalanced (ie. there are
> very different numbers of observations for different people, taken at
> different times), the main relationship of interest involves a time-varying
> predictor, and one of the variables with missing data is not continuous (it
> is a binary, categorical variable).  So, I will stick with the multiple
> imputation approach for handling the missing data.

As Wolfgang mentioned, OpenMX can fit a FIML analysis to irregular data. 
If you were, for example, interested in a profile likelihood around a 
variance component, that might be the way to go.  It seems to me that 
multiple imputation might not always respect complicated 
clustering/correlation, depending on the actual method. A quick search 
found some cautionary tales in:

http://www.bmj.com/content/338/bmj.b2393.extract

Just another 2c, David.


| David Duffy (MBBS PhD)
| email: David.Duffy at qimrberghofer.edu.au  ph: INT+61+7+3362-0217 fax: -0101
| Genetic Epidemiology, QIMR Berghofer Institute of Medical Research
| 300 Herston Rd, Brisbane, Queensland 4006, Australia  GPG 4D0B994A